Head mounted video and touch detection for healthcare facility hygiene

ABSTRACT

A system and method tracks touches in a healthcare environment in order to analyze paths of transmission and contamination for the purpose of eliminating and containing transmission of colonizing, drug-resistant pathogens. Touches are identified and tracked with the use of recording devices. Each touch is logged and a touch graph is generated to identify transmission paths.

FIELD OF THE INVENTION

The invention disclosed broadly relates to the field of healthcarehygiene, and more particularly relates to the field of touch detectionto prevent contamination and spread of bacteria.

BACKGROUND OF THE INVENTION

Infections are a large and growing problem in healthcare environmentstoday. Many healthcare environments such as hospitals are plagued byantibiotic-resistant bacteria. The demands on healthcare workers includeconstant vigilance for contaminated surfaces and opportunities fortransmission, as well as costly management of patients who are carriers.Healthcare workers themselves can become colonized by these strains ofmicroorganisms, and face job loss as a result.

Germs can live on surfaces for a long time. Some gram-positive bacteriacan survive for months on dry surfaces. Blood-borne pathogens, such asHBV and HIV, can live for days outside of the body. Some of the mostcommon nosocomial pathogens may well survive or persist on surfaces formonths and can thereby be a continuous source of transmission if noregular preventive surface disinfection is performed.

As workloads increase with cost-cutting measures in the healthcarespace, the ability of workers to manage the cognitive load of attendingto and tracking contact with all surfaces in order to maintain properhygiene and prevent the spread of drug resistant bacteria is thereforechallenged. Hand hygiene is a simple solution and studies show thatproper hand hygiene is a huge factor in thwarting the spread of germs.Even though hand hygiene is well known as an important step inhealthcare vigilance, and reminders are ubiquitous in hospital settings,hospital workers are not always aware of everything they touch. Also,the quality of hand washing is a factor. Plus, compliance is a problembecause hand-washing is a self-regulated act.

Typically, all of the control mechanisms in facilities today depend onthe vigilance of healthcare workers, and the existing solutions inpractice today are therefore extremely vulnerable. If only one workerfails to execute proper hygiene the entire system is threatened. Thereis a need for a system and method to overcome the above-statedshortcomings of the known art.

SUMMARY OF THE INVENTION

Briefly, according to an embodiment of the present disclosure, a methodtracks touches in order to analyze paths of transmission andcontamination for the purpose of eliminating and containing transmissionof colonizing, drug-resistant pathogens. The method can be implementedin a healthcare setting, in a school, subway, or in any environmentwhere public safety is a major concern. In this method, touches areidentified and tracked with the use of recording devices. Each touch islogged and a touch graph is generated to identify transmission paths.

According to another embodiment of the present disclosure, a touchdetection system tracks touches in a healthcare environment in order toidentify transmission paths of deleterious entities. The system includesvarious components such as forward-facing recording devices, PANs, andprocessing components for performing analysis and recommendingmitigating actions. The deleterious entities can be any of:microorganisms, toxins, anthrax spores, ricin, or the like. Therecording devices and PANs not only record the touches, but are used toincrease the confidence level of object identification, as well asproviding data about the nature of the risk involved.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To describe the foregoing and other exemplary purposes, aspects, andadvantages, we use the following detailed description of an exemplaryembodiment of the disclosure with reference to the drawings, in which:

FIG. 1A is an exemplary depiction of a healthcare provider wearing ahead-mounted display, according to the known art;

FIG. 1B is a close-up view of a head-mounted display, according to theknown art;

FIG. 2 is a simplified block diagram showing the components of the touchdetection and analysis system, according to an embodiment of the presentdisclosure;

FIG. 3 is a high-level flowchart of the touch detection and analysismethod, according to an embodiment of the present disclosure;

FIG. 4 is an exemplary illustration of a touch graph, according to anembodiment of the present disclosure;

FIG. 5 is a high level block diagram showing an information processingsystem configured to operate according to an embodiment of the presentdisclosure;

FIG. 6 is a lower-level flowchart of the touch detection and analysismethod, according to an embodiment of the present disclosure; and

FIG. 7 is a simplified illustration of how a PAN works, according to theknown art.

While embodiments of the disclosure, as claimed, can be modified intoalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that the drawings and detaileddescription thereto are not intended to limit the disclosure to theparticular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thescope of the present disclosure.

DETAILED DESCRIPTION

Before describing in detail embodiments that are in accordance with thepresent disclosure, it should be observed that the embodiments resideprimarily in combinations of method steps and system components relatedto systems and methods for placing computation inside a communicationnetwork. Accordingly, the system components and method steps have beenrepresented where appropriate by conventional symbols in the drawings,showing only those specific details that are pertinent to understandingthe embodiments of the present disclosure so as not to obscure thedisclosure with details that will be readily apparent to those ofordinary skill in the art having the benefit of the description herein.Thus, it will be appreciated that for simplicity and clarity ofillustration, common and well-understood elements that are useful ornecessary in a commercially feasible embodiment may not be depicted inorder to facilitate a less obstructed view of these various embodiments.

Definitions of terms used throughout this disclosure:

augmented reality coloration—a technology that assigns different colorsto different objects or views of objects; used with augmented realitydevices such as head-mounted displays

deleterious entity—a chemical, physical, or biological entity that cancause harm

gram-positive bacteria—multi-drug resistant bacteria that are staineddark blue by gram staining

HBV—Hepatitis B virus

HIV—Human Immunodeficiency Virus

HMD—head-mounted display—an augmented reality hands-free wearablecomputing device

LCD (liquid crystal display)—an electronic visual display

LCoS (liquid crystal on silicon)—an electronic visual display using aliquid crystal layer on silicon

MSRA—Methicillin-resistant Staphylococcus aureus causes infections thatare tougher to treat than most staph infections because of itsresistance to commonly-used antibiotics nosocomial infection—aninfection originating in a hospital

OLED (organic light-emitting diode)—a visual display medium

pathogen—an infectious agent that can produce a disease

PAN (personal area network)—near-field intra-body communication

ricin—a deadly toxin that is the byproduct of making castor oil

We describe a method, service, and system that tracks touches in ahealthcare environment in order to analyze paths of transmission andcontamination for the purpose of automatically limiting transmission ofcolonizing, drug-resistant pathogens. By automatically comparing pathsof transmission with real-world detection of microorganisms on surfacesof objects and the skin of workers, a means is disclosed toautomatically identify problem areas that can be targeted for improvedhygiene. To this end we combine: 1) head-mounted forward-facing videocameras (e.g. Google Glass™) used by healthcare workers such as doctorsand nurses; 2) object recognition applied to the video stream, and 3)the automated creation and analysis of a contact graph. This approach islikely to save hospitals money in the long run due to reduced costs,reduced disease incidence, and reduced liability, to name a few. Just asimportant, many hospital workers will consider this as an aid infollowing correct procedures and also help create a healthier workingenvironment for the workers themselves.

Currently, there is no known solution that makes use of thesemulti-disciplinary techniques in a healthcare environment as a servicefor the purpose of automatically limiting transmission of colonizing,drug-resistant bacteria. One with knowledge in the art will appreciatethat the embodiments discussed herein, although focused on healthcareenvironments for discussion purposes only, can be implemented in otherenvironments as well. For example, in this age of terrorism, theembodiments described herein can be advantageously implemented to thwartthe transmission of toxins, ricin, anthrax spores, and the like in masstransit stations, airports, and other environments where the safety ofthe general population can be at risk.

FIGS. 1A and 1B—Video Recording Device.

Referring now to FIG. 1A there is shown a depiction of a health-careprovider wearing a head-mounted display (HMD) 100 with a built-in cameraand touchpad, as is known in the art. HMDs are wearable intelligentdevices, worn by the user like a pair of eyeglasses. They displayinformation and interact with the Internet in a hands-free format, usingnatural language voice commands. FIG. 1B is a close-up image of the HMD100 showing that a typical HMD 100 includes a casing 120 in the form ofa headband, eyeglasses or goggles, as well as components 150 normallyfound in smart phones, such as a camera, a processor, a networkinterface, and a micro display.

Thanks to technological advances in computing and photographic devices,audio and video recording devices are getting smaller. In anotherembodiment of the present disclosure, the audio and video recordingdevice is not a head mounted display, but is instead a small,forward-facing camera device worn on a lanyard or clothing of thehealthcare worker. The camera device can even be coupled with thehealthcare worker's ID card.

FIG. 2—Touch Detection and Analysis System.

Referring now to the drawings in general and to FIG. 2 in particular, weshow additional components of the Touch Detection and Analysis System200, according to an embodiment of the present disclosure. Although notall of the following components are required to operate the TouchDetection and Analysis System 200 together with the HMD 100, they areprovided below for completeness so as to facilitate a working System200.

PAN—Image and Near-field Intrabody Analysis Component for Object/SurfaceRecognition 210. In one embodiment, healthcare workers in a healthcareenvironment are assigned unique IDs and equipped with a recording devicethat records objects and patients in the worker's environment andcatalogues these in a database shared among workers. Each object isassigned an object ID. Touchable surfaces are identified and each isassigned a surface ID. Each patient is also assigned a patient ID. As anexample, nurse 0038, janitor 1032, and physician 0005 may or may notinteract a number of times with heart monitor 0010, bed rail 1013, andsink handle 0105.

As a further example, “objects” may also include people who may touchsurfaces, and the nature of this contact may be gleaned, in part, byboth a video system and a PAN (Personal Area Network) system that usesnear-field intrabody communication as is known in the art. Referring nowto FIG. 7, a PAN 700 is a network consisting of electronic devices thatare commonly found on or near a human body, such as cellular phones,personal digital assistants (PDAs), and the like. Normally these devicesdo not share information, but within a PAN, these electronic devicesoperate to exchange digital information using the human body's ownelectrical currents 750 as transmission paths. A PAN near-fieldintrabody communication system uses the human body as an electricalmedium, capacitively coupling the picoamp currents 750 that naturallyrun through the human body. A PAN Transmitter 710 with a TransmitterElectrode 712 act as a capacitor (Virtual Capacitor A 715), capacitivelycoupling current 750 through the human body to a PAN Receiver 720. TheReceiver Electrode 722, with the Receiver 720, act as a capacitor(Virtual Capacitor B 725).

Another Receiver Electrode 726 facing Ground 790 act as Capacitor C,while another Transmitter Electrode 716 facing Ground 790 acts asCapacitor D. Electric fields 730 are thus generated between the Ground790 and the electrodes 726 and 716. The mechanics of a PAN near-fieldintrabody communication system are beyond the scope of this disclosure.More information can be found in “Personal Area Network: Near-fieldintrabody communication” by T. G. Zimmerman published in IBM SystemsJournal, Vol. 35, Nos. 3&4, 1996, which is incorporated by referenceherein in its entirety. An example of a near-field intrabody system inuse today allows users to exchange electronic business cards by shakinghands.

In this disclosure, we use streaming video data from multiple devicessuch as HMDs 100 and stationary cameras. The video data is collected andstored from each device. Because of the extremely large volume ofpotential video data, intelligent compression and filters are applied toreduce inconsequential (low risk) segments. An example possible area fordata reduction may involve a hospital staff person who is walking down ahallway where no contact is made either with surfaces or other personsin a defined radius R, for a time period T, with a given considerationof possible transmission characteristics (e.g. airborne pathogentransmission).

In addition, the streaming video data may be processed in real-time andstored with metadata to identify or indicate “persons” and “locations”in the current stream (with optional use of facial recognitiontechnology to identify staff and/or non-staff persons). With the videodata available in this manner, crime-scene like techniques can be usedto identify and follow the web of potential pathogen transmissionsources, points of contact, and routes. Using analytic methods known inthe art, a well-defined action plan may optionally be rapidly assembledto combat or respond to the potential threat. Based on informationavailable, the HMD may be able to warn the wearer of potential problems(i.e., infected surfaces).

Image Analysis Component for Touch Detection 220. Cameras record thepositions of workers' hands in the environment. The camera can be partof a HMD 100. Touches with surfaces are recorded and identified, and theassociated object ID retrieved from a database 225. Objects' surfacesand patients determined to have been touched are entered in a touchdatabase 225 according to the time, worker ID, object/patient ID, andsurface ID. Touch detection may be facilitated by various meansincluding any of: image analysis, bar code analysis, RFID analysis, QRcode analysis, contact sensors, and the like. Similarly, personal areanetworks (PAN) using near-field intrabody communication can be used tofacilitate aspects of touch characterization. We make use of variousmeans to collect real-time touch data, including both head mounteddisplays (HMDs) 100, such as Google Glass™, and PANs 700 to facilitateaspects of touch characterization and boost the confidence level C ofobject identification in a complex and dynamically changing environment.

Touch Database 225. A Touch Database 225 is used for logging eachoccurrence of a touch. Each touch is logged with at least a timestamp,worker ID, object/patient ID, and surface ID. The Touch Database 225 maybe separate or incorporated into the database storing the IDs.

Image and Audio Analysis Component for Coughing and Sneezing 230. Thiscomponent can be employed to aid in identifying problem regions in ahealthcare setting, along with timing information related to the time ofpotential contamination. We take advantage of cough-detection andsneeze-detection methodologies that are known in the art, such as thatdescribed in U.S. Pat. No. 8,241,223, “Cough Detector,” issued to iSoneaLimited on Aug. 14, 2012. In one method of cough detection, an algorithmidentifies loud sounds with a cough pattern to search for cough“candidates.” In another method, a cough detector worn around the neckuses ultrasound to detect coughs.

Touch Graph Creation Component 240. Using the touch occurrences loggedin the Touch Database 225, we formulate a graph representation of thetouch occurrences (shown in FIG. 4). Healthcare environment workers 410,patients and visitors 420, and object surfaces 430 (such as hospitalequipment and furnishings) are each assigned a node on the Graph 400,with touches 450 represented as edges in the graph representation. Edgesare bidirectional to represent potential transmission 460 ofmicroorganisms in either direction during contact.

Contamination Test Database 250. Based either on a random samplingtechnique, or on specific samples ordered by the outputs of analyses oflikely transmission paths, tests for contamination of objects' surfaces,patients, and workers are entered into the Database 250 and associatedwith their corresponding IDs and graph nodes from the Touch Graph.

Automated Transmission Simulation Component 260. When contamination witha single strain of bacteria is detected in two or more nodes, allpossible transmission paths are automatically calculated using the TouchGraph. Traversal of this graph is time-dependent, in that transmissionmust occur in temporal order across two or more identified touches. Eachpath is entered in a transmission path database.

Automatic Test of Candidate Paths Component 270. The most parsimonious(fewest nodes/touches) path is then optionally identified from thedatabase as the primary hypothesis, and a test of this hypothesis isordered, involving a directed sample of a non-tested node in thetransmission path, if possible. If the test is negative, the second mostparsimonious path is tested, and so on.

Transmission Path Correlation Component 280. Over time, multiplepositively tested transmission paths in the environment will beidentified and noted in the transmission path database. These are thenreanalyzed for correlated nodes to determine which nodes show asystematic and temporally correlated high likelihood for a role intransmission. These suspected paths are then ranked based on mitigationbenefit, M, which is equal to a function f of the likelihood of the pathplaying a role in the transmission of pathogens, L, the expected impactof a mitigating action on reducing transmission, P, and any other costs,benefits, or risks associated with mitigation, C, i.e., M=f(L,P,C).

Note that each visitor may also be optionally assigned a trackingnumber. This service may be used to indicate what not to touch due topossible contamination. This could be indicated by various means, inreal-time, as confidence level about problematic locations, devices,vents, buttons, monitors, IV poles, door knobs, faucets, or otherobjects increases. One may optionally flag a person walking around asbeing problematic. Note that analytics may be provided to determine theprobability of a contaminated path, based on anonymized data of thepatient's records, healthcare provider contacts, visitor contacts, andpath taken or to be taken.

Mitigating Action Component 290. Mitigating actions are optionally thentaken, directed at nodes identified as having the highest mitigationbenefit, M. These actions can include scheduled sterilization ofsurfaces, isolation of a patient, or targeted education of health careworkers on proper hygiene. Over time, multiple, positively-testedtransmission paths in the environment will be identified and noted inthe transmission path database. These are then reanalyzed for correlatednodes to determine which nodes show a systematic and temporallycorrelated high likelihood for a role in transmission. These suspectedpaths are then ranked based on mitigation benefit, M, which is equal toa function f of the likelihood of the path, L, the expected impact of amitigating action on reducing transmission, P, and any other costs,benefits, or risks associated with mitigation, C, i.e., M=f(L,P,C).

An additional mitigating action may optionally include advising healthcare workers on movement paths through the hospital for them, patients,and visitors so as to minimize risk of transmission. Not only does theservice automatically indicate problem paths (e.g. paths that might beavoided or cleaned, or avoided until cleaned with an automatic triggerto cleaning staff), but the system may optionally can show paths thatare likely to be good (with a confidence level). Of course, a “good”path might cross the threshold to “bad” after N people have traversedit, with N being a large number, and if the people traversing the pathhave touched objects with possible problems.

The head-mounted display 100 may optionally specify the nature of aproblem that concerns a traversal path, a contaminated object, a personto avoid, a hallway to avoid, a hallway to use, an object to use, etc.For example, augmented reality coloration can suggest, with confidencelevel C, that a path, object, or person is associated with one or moremicroorganisms, toxin, sterility (or lack of sterility) level, and thelike. For example, different colors can be applied to different objectsin order to easily distinguish among types of objects. This kind ofoptional mitigating component may based on extending the use of the headmounted displays beyond collecting data on objects touched by healthcare workers. The service may also use the head mounted displays to alsoprovide feedback to the health care workers on what not to touch. Oneintriguing aspect of the use of HMDs 100 is the means to integrate datafrom the forward facing camera based analysis of transmission with areal-time feedback from the HMD 100. For both visitors and health careworkers this seems like a valuable addition to the invention

Mitigation Action Evaluation Component 292. After a mitigating action isoptionally taken, follow-up analyses of transmission paths may determineif a change in the transmission topology through the touch graph isrealized by the action. If no change in transmission topology occurs, Pis adjusted for those nodes previously mitigated, and a new mitigatingaction determination is ordered, based on the new findings.

Machine-Learning Component 295. Over time, the evaluation of mitigatingactions may optimally be identified by machine learning. Machinelearning can provide the indication that certain classes of candidatehypothetical transmission paths are unlikely, despite correlations inthe touch graph. The graph can be updated (for example, even though itappeared that person 0001 was contaminated, later tests may reveal thisto be inaccurate), but just as likely, this more directly changes thecalculation of M, as indicated in the following sentences. Mitigatingactions are optionally then taken, directed at nodes identified ashaving the highest mitigation benefit, M. These may be due to theidentity of the node, or other contextual inputs to the machine learningcomponent. These determinations are then fed back into the calculationof the L and/or P for these nodes, the tough graph is updated, shapingfuture calculations of M. Analytics may be provided to determine theprobability of a contaminated path, based on anonymized data of thepatient's record, healthcare provider/visitor contact and path to betaken.

Head-mounted forward-facing video cameras are coupled with the Image andNear-field Intrabody Analysis Component for Object/Surface Recognition210. These HMDs 100 come in many forms and form factors and the presentembodiments cover a range of such wearable devices that may include oneor two small displays with lenses, with semi-transparent mirrorsembedded in a helmet, eye-glasses, visors, CRTs, LCDs, liquid-crystal onsilicon (LCoS), OLED, “curved Mirror” HMDs, and “waveguide” HMDs, toname a few. Advanced head-mounted displays, including displays thatresemble glasses such as Google Glass™, are becoming more popular.

FIG. 4—Transmission Graph. Referring now to FIG. 4, we provide anexemplary Transmission Graph 400. The Transmission Graph 400 includesnodes representing workers 410, patients and visitors 420, and touchedobjects 430. The touches among the nodes are the edges 450 of the graph.The transmission paths 460 are the set of connected nodes that providethe highest probability of contamination, shown here as a bold line. TheTransmission Graph 400 provides a good visual indicator of those pathswhich are likely to be transmission paths for pathogens (candidatetransmission paths 460) Likewise, the Transmission Graph 400 provides agood visual indicator of paths that are not problematic.

Service

The touch detection and analysis method according to this disclosure canbe provided as a service for a fee. The service presented here has aninitial up-front cost, however it is likely to save hospitals money inthe long run due to reduced costs, disease incidence, liability, andother factors, while aiding hospital workers. The service disclosedherein employs several components that automatically implement thetouch-detection and touch-graph analysis in order to identify candidatepaths for transmission of microorganisms, and correlates and tests thesecandidate paths against observed paths of spread of contamination in thehealthcare setting, or in any setting where the health and welfare of alarge population of people is at risk.

FIG. 3—Flowchart.

Referring now to FIG. 3 we show a high-level flowchart of a method fortouch detection and analysis, according to one embodiment of the presentdisclosure. In step 310, we employ head mounted, hands-free opticaldisplays outfitted with cameras, such as Google Glass™, to identify andrecord touches. By using these devices, we facilitate and increase theconfidence of real-time touch data collection. In step 320, we log eachrecorded touch in a data store 225, noting the identifying informationfor each touch.

From the touch logs 226, we generate the Transmission Graph 400 to trackpathogen transmission in step 330. The nodes in the Graph 400 representhospital workers, patients, visitors, and touched objects, among otherpoints of possible contact. In step 340 we identify any “problem nodes”and perform an automatic calculation of the impact of a mitigatingaction, with the automatic evaluation of likelihood of transmissionbased on automatically ordered hypothesis testing. We define “problemnodes” as those nodes that can be targeted for improved hygiene. Problemnodes are nodes that have a potential for being associated with adeleterious event such as pathogen or other harmful transmission. Moreparticularly, these may be nodes with a preponderance of evidencepointing to involvement in one or multiple transmissions, such as anurse who doesn't practice good hand washing.

In order to identify the problem nodes, we perform a sampling ofsurfaces, patients, and workers for contamination. The sampling can beperformed in an automated fashion, such as through the use of manyapproaches known in the related art to detect microorganisms, which maybe implemented by ambient devices. Some examples of these approachesare:

a) The use of detection devices (“polymers that fluoresce in thepresence of bacteria, paving the way for the rapid detection andassessment of wound infection using ultra-violet light.”)

b) Laser Nanosensors that detect Bacteria

c) other approaches that may be deployed in an automated fashion usedrones, robots, sensors, and devices on surfaces. One such approach isdescribed in Patent WO 2013057731 “Methods of detecting the presence ofmicroorganisms in a sample,” by D.I.R. Technologies.

In step 350 the system automatically directs an appropriate mitigatingaction with follow-up analysis of effects on future transmission, aswell as machine learning association of context with the action, forfuture refinement to the expected impact of mitigation. As previouslystated, mitigating actions can include: advising health care workers onmovement paths of pathogens; scheduled sterilization of surfaces;isolation; and targeted education of health care workers.

Referring now to FIG. 6, we show a lower-level flowchart 600 of thetouch detection and analysis method, according to an embodiment of thepresent disclosure. In step 610, we collect video and/or audio data ofinteractions among healthcare workers, patients and objects. We canemploy forward facing head mounted cameras (HMDS) worn by health careworkers. In step 615 we analyze the video for object surfacerecognition, surface touches, and patient touches by workers. Theanalysis can be performed by the HMD itself.

In step 620 all objects, surfaces, patients, workers, touches, and timeof touches are automatically entered into touch logs 226, once they areidentified. In step 625 the touch log data is analyzed to generate aTransmission Graph 400 wherein nodes correspond to object surfaces,patients, and workers, and touches/touch times correspond to edges 450in the Transmission Graph 400.

In step 630 surfaces, patients, and workers can be sampled to test forcontamination by identifiable strains of microorganism. The sampling canbe performed randomly, at periodic intervals, or a targeted sampling canbe performed in response to a specific indication of contagion. Forexample, if a case of MRSA is discovered on the fourth floor of ahospital, then a targeted sampling of all workers, patients, and objectson the fourth floor could be ordered.

In step 635 after we receive the results of the sampling, we annotatethe database 225 and Transmission Graph 400 with contaminationinformation. In step 640, for any two or more contaminated nodes, wedetermine all possible temporally ordered paths of transmission 460between nodes and enter these paths 460 in the database 225. In step645, we deploy tests to narrow candidate paths for transmission 460. Asjust one example, the tests mentioned above that concern automatedsampling by drones or robots can be used to boost the accuracy andconfidence level of possible contaminants. The HMDs can be employed toboost confidence levels as well. For example, nearby regions or the sameregions may be retested, which may be useful in an environment that isconstantly changing and for which transmission paths may be changing.

In step 650, we correlate candidate paths to determine problem nodes inthe Transmission Graph 400. In step 655, we calculate a mitigationbenefit M, as a function of a likelihood of the candidate path L beingthe transmission path 460, the expected impact from a mitigating actiondirected at reducing transmission P, and any other costs, benefits, orrisks associated with the mitigation C. The formula is: M=f(L,P,C). Thesuspected, or candidate, paths, are then ranked based on the calculatedmitigation benefit, M. Paths may be analyzed and re-analyzed forcorrelated nodes to determine which nodes show a systematic andtemporally correlated high likelihood L for a role in transmission.

In step 660, we deploy at least one mitigating action against nodes withthe highest M values. Possible mitigating actions can be: a) cover asurface with antimicrobial film; b) schedule regular sterilization ofequipment; c) ask visitors to don face masks before entering a patient'sroom; and d) use a germ-detecting UV (ultraviolet) light. In step 665,we analyze subsequent candidate transmission topology to determine ifthe mitigating action has had an effect. Then, we adjust expected futureimpacts of mitigating actions, M, accordingly. In step 670, we associateother contextual cues retrospectively with expected impacts of futuremitigating actions M using machine learning. In step 675, we adjust thelikelihood of transmission L through the nodes, and/or expected futureimpacts of mitigating actions, P, according to learned contexts.

Benefits and Advantages of the Disclosure

The current disclosure has several advantages over known solutions formanaging transmission of microorganisms in a healthcare environment,such as:

1. the coordinated use of multi-disciplinary technology such ashead-mounted forward-facing cameras and sneeze detectors to collectreal-time touch data;

2. creation of transmission graphs with automatic evaluation oflikelihood, based on automatically ordered hypothesis testing;

3. identification of problem nodes and automatic calculation of theimpact of a mitigating action; and

4. automatic ordering of mitigating action with follow up analysis ofeffects on future transmission, as well as machine learning associationof context with the action, for future refinement to the expected impactof mitigation.

FIG. 5—Hardware.

Referring now to FIG. 5, there is provided a simplified pictorialillustration of the components of an information processing system 500for touch detection and analysis in which embodiments of the presentdisclosure may be implemented. For purposes of this disclosure, computersystem 500 may represent any type of computer, information processingsystem or other programmable electronic device, including a clientcomputer, a server computer, a portable computer, an embeddedcontroller, a personal digital assistant, Cloud computing, Internet TV,and so on. The computer system 500 is a networked computing device incommunication with other computing devices such as the HMDs (not shown)via a network. As will be appreciated by those of ordinary skill in theart, the network may be embodied using conventional networkingtechnologies and may include one or more of the following: local areanetworks, wide area networks, intranets, public Internet and the like.

Throughout the description herein, an embodiment of the disclosure isillustrated with aspects of the disclosure embodied solely on computersystem 500. As will be appreciated by those of ordinary skill in theart, aspects of the invention may be distributed amongst one or morenetworked computing devices which interact with computer system 500 viaone or more data networks such as, for example, network 510. However,for ease of understanding, aspects of the invention have been embodiedin a single computing device-computer system 500.

Computer system 500 includes inter alia, processing device 502 whichcommunicates with an input/output subsystem 506, memory 504, storage 510and network 510. The processor device 502 is operably coupled with acommunication infrastructure 522 (e.g., a communications bus, cross-overbar, or network). The processor device 502 may be a general or specialpurpose microprocessor operating under control of computer programinstructions 532 executed from memory 504 on program data 534. Theprocessor 502 may include a number of special purpose sub-processorssuch as a comparator engine, each sub-processor for executing particularportions of the computer program instructions. Each sub-processor may bea separate circuit able to operate substantially in parallel with theother sub-processors.

Some or all of the sub-processors may be implemented as computer programprocesses (software) tangibly stored in a memory that perform theirrespective functions when executed. These may share an instructionprocessor, such as a general purpose integrated circuit microprocessor,or each sub-processor may have its own processor for executinginstructions. Alternatively, some or all of the sub-processors may beimplemented in an ASIC. RAM may be embodied in one or more memory chips.

The memory 504 may be partitioned or otherwise mapped to reflect theboundaries of the various memory subcomponents. Memory 504 may includeboth volatile and persistent memory for the storage of: operationalinstructions 532 for execution by CPU 502, data registers, applicationstorage and the like. Memory 504 preferably includes a combination ofrandom access memory (RAM), read only memory (ROM) and persistent memorysuch as that provided by a hard disk drive 518. The computerinstructions/applications that are stored in memory 504 are executed byprocessor 502. The computer instructions/applications 532 and programdata 534 can also be stored in hard disk drive 518 for execution byprocessor device 502. Those skilled in the art will appreciate that thefunctionality implemented within the blocks illustrated in the diagrammay be implemented as separate components or the functionality ofseveral or all of the blocks may be implemented within a singlecomponent.

The computer system 500 may also include a communications interface 412.Communications interface 512 allows software and data to be transferredbetween the computer system and external devices. Examples ofcommunications interface 512 may include a modem, a network interface(such as an Ethernet card), a communications port, a PCMCIA slot andcard, etc. Software and data transferred via communications interface512 are in the form of signals which may be, for example, electronic,electromagnetic, optical, or other signals capable of being received bycommunications interface 512.

Computer Program Product.

Also with reference to FIG. 5, embodiments of the present disclosure maybe a system, a method, and/or a computer program product. The computerprogram product may include a computer readable storage medium 520 (ormedia) having computer readable program instructions thereon for causinga processor to carry out aspects of the present disclosure.

The computer readable storage medium 520 can be a tangible device thatcan retain and store instructions for use by an instruction executiondevice. The computer readable storage medium 520 may be, for example,but is not limited to, an electronic storage device, a magnetic storagedevice, an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium 520, as used herein, is not to beconstrued as being transitory signals per se, such as radio waves orother freely propagating electromagnetic waves, electromagnetic wavespropagating through a waveguide or other transmission media (e.g., lightpulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium 520 or to an external computer or externalstorage device via a network, for example, the Internet, a local areanetwork, a wide area network and/or a wireless network. The network maycomprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions 532 for carrying out operationsof the present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language, and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions 532 may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions 532 may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium 520 having instructions stored therein comprisesan article of manufacture including instructions which implement aspectsof the function/act specified in the flowchart and/or block diagramblock or blocks.

The computer readable program instructions 532 may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions 532for implementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Therefore, while there have been described embodiments of the presentdisclosure, it will understood by those skilled in the art that othermodifications can be made within the spirit of the disclosure. The abovedescriptions of embodiments are not intended to be exhaustive orlimiting in scope. The embodiments, as described, were chosen in orderto explain the principles of the disclosure, show its practicalapplication, and enable those with ordinary skill in the art tounderstand how to make and use the disclosure. It should be understoodthat this disclosure is not limited to the embodiments described above,but rather should be interpreted within the full meaning and scope ofthe appended claims.

We claim:
 1. A method for touch detection and analysis, comprising usinga processor device, performing steps of: receiving from a first device,data comprising an indication that a touch has occurred within a settingcomprising a plurality of identified persons, a plurality of identifiedobjects, and a plurality of identified surfaces; automatically analyzingthe data to identify the touch; logging the touch occurrence in a touchstore; generating a transmission graph from logged touches to identifydeleterious entity transmission paths, said transmission graphcomprising: a node representing each person, each object, and eachsurface involved in the touch occurrence; and edges between the nodesrepresenting the touch occurrence; and analyzing the touch graph toidentify candidate transmission paths.
 2. The method of claim 1 furthercomprising using the processor device to perform proposing a mitigatingaction to prevent the deleterious entity transmissions, wherein amitigation benefit derived from performing said mitigating action is afunction of the likelihood of the transmission path playing a role insaid transmission of the deleterious entity, the expected impact of thesaid mitigating action on reducing transmission, and any other costs,benefits or risks associated with said mitigating action.
 3. The methodof claim 2 further comprising using the processor device to performevaluating effects of the mitigating action.
 4. The method of claim 3further comprising using the processor device to perform updating thetouch graph according to the evaluation.
 5. The method of claim 1wherein analyzing the possible deleterious entity transmissions from thetouch graph comprises performing a sampling of at least one of: theplurality of identified persons, the plurality of identified objects,and the plurality of identified surfaces represented in the touch graphto test for contamination.
 6. The method of claim 5 further comprising:receiving results of the sampling; and using the results toautomatically generate a set of candidate transmission paths of thedeleterious entity.
 7. The method of claim 3 further comprising rankingthe candidate transmission paths based on the mitigation benefit.
 8. Themethod of claim 1 wherein receiving the data from the first devicecomprises receiving touch identification from a touch identificationsystem comprising at least one of: a head-mounted display camera and anintrabody near-field communication network.
 9. The method of claim 8further comprising using the touch identification system to increaseconfidence levels in the identification of the candidate transmissionpaths from the touch graph.
 10. An information processing system fortouch detection and analysis comprising: a processor device; a memoryoperably coupled with the processor device, said memory comprisingcomputer-executable instructions for performing: receiving from a firstdevice, data comprising an indication that a touch has occurred within asetting comprising: a plurality of identified persons, a plurality ofidentified objects, and a plurality of identified surfaces;automatically analyzing the data to identify the touch; logging thetouch occurrence in a touch store; generating a transmission graph fromlogged touches to identify deleterious entity transmission paths, saidtransmission graph comprising: a node representing each person, eachobject, and each surface involved in the touch occurrence; and edgesbetween the nodes representing the touch occurrence; and analyzing thetransmission graph to identify candidate transmission paths.
 11. Theinformation processing system of claim 10 wherein thecomputer-executable instructions further comprise ranking the candidatetransmission paths by likelihood of transmission.
 12. The informationprocessing system of claim 10 wherein the computer-executableinstructions further comprise proposing a mitigating action to preventthe deleterious entity transmissions, wherein a mitigation benefitderived from performing said mitigating action is a function of thelikelihood of the transmission path playing a role in the transmissionof the deleterious entity, the expected impact of the said mitigatingaction on reducing transmission, and any other costs, benefits or risksassociated with said mitigating action.
 13. The information processingsystem of claim 10 wherein the first device is a touch identificationsystem comprising at least one of: a head-mounted display and anintrabody near-field communication network; and wherein the data is acombination of video data and audio data.
 14. The information processingsystem of claim 13 wherein the first device is to increase confidencelevels in the identification of candidate transmission paths.
 15. Theinformation processing system of claim 10 wherein the setting comprisesa healthcare facility.
 16. The information processing system of claim 10wherein analyzing the transmission graph further comprises determiningthe transmission paths that are not candidate transmission paths forpathogens.
 17. A method for automatically identifying transmission pathsof deleterious entities in a health care environment, said methodcomprising: using a processor device coupled with a recording device,performing: collecting video and audio data from the recording deviceworn by a health care worker; analyzing the video and audio datacollected from the recording device; identifying touched objects,surfaces, and patients from the analysis; generating a transmissiongraph with nodes representing the touched objects, surfaces, andpatients; and analyzing the transmission graph to identify candidatetransmission paths of the deleterious entities.
 18. The method of claim17 further comprising ranking the candidate transmission paths bylikelihood of transmission.
 19. The method of claim 18 furthercomprising recommending a mitigating action as a function of thelikelihood of the candidate transmission path playing in thetransmission of the deleterious entities, the expected impact of themitigating action on reducing transmission, and any other costs,benefits or risks associated with said mitigating action.
 20. The methodof claim 19 further comprising performing an automatic evaluation ofmitigation impact to modify future impact predictions.